Forecasting Energy Consumption in Smart Grids: A Comparative Analysis of Recurrent Neural Networks

In the present era of smart grids, accurate prediction of energy uses is becoming increasingly essential to guarantee optimal energy efficiency. This study contributes to the field by utilizing advanced machine learning techniques to perform predictions of energy consumption using the data from Internet of Things (IoT) devices. Specifically, the approach utilizes regression neural network (RNN) structures, such as long short-term memory (LSTM) and gated recurrent units (GRUs). The data from IoT sensors are more extensive and detailed than those of conventional smart meters, allowing for the development of more complex models of energy use patterns. This study utilizes Adam-optimized LSTM, RNN, and GRU models, along with stochastic gradient descent, to evaluate their performance in addressing the complexity of time-series data in energy forecasting on different network configurations. Result of the analysis indicates that LSTM models, which are run with the Adam optimizer, are more accurate in terms of predictions compared with the other models. This conclusion is supported by the test results of these models that are within the lowest root mean square error and mean absolute error scores. All the models under the analysis exhibit signs of overfitting based on the performance indicators for the training and the testing data. This notion implies that regularization should be utilized to ensure the improved generalizability of the models. These findings show that deep learning can have a lasting influence in improving energy consumption management systems to meet sustainability and energy efficiency requirements. These observations are beneficial for the gradual improvements of smart grids.